Adaptive Data Sharing and Computation Offloading in Cloud-Edge Computing with Resource Constraints

Collaborative tasks require the participation of multiple agents. Each agent in collaboration needs sufficient data to make optimal decisions. However, in general, each agent can only collect and process a limited amount of data due to resource constraints. Peer-to-peer data sharing can enrich local observations, but a particular agent may not have enough resources to adequately store and process data, thus compromising group decision making. Cloud-Edge Computing (CEC) can relieve agents of these limitations by providing them with further storage and computing resources through connected cloud-like infrastructures. However, CEC-based collaborations currently face two key challenges: 1) lack of adaptability to resource restrictions in data sharing; 2) no support of offloading non-trivial tasks with complex data dependencies. This paper proposes an approach to realize adaptive data sharing and support computation offloading. Roughly speaking, the paired parameterized-structure is designed based on data flow analysis and bidirectional transformations to benefit adaptive data synchronization and offloading. And a hybrid offloading mechanism is offered for allocating computations among agents and the cloud, regarding data dependencies and restrictions. We demonstrate the feasibility and flexibility through a collaborative victim search and rescue case. Experiments show that our approach outperforms state-of-the-art methods.